Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology
To explore the application of near-infrared (NIR) technology to the quality analysis of raw intact tobacco leaves, a nondestructive discrimination method based on NIR spectroscopy is proposed. A “multiregion + multipoint” NIR spectrum acquisition method is developed, allowing 18 NIR diffuse reflecta...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2021/8807199 |
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| author | Mengyao Lu Qiang Zhou Tian’en Chen Junhui Li Shuwen Jiang Qin Gao Cong Wang Dong Chen |
| author_facet | Mengyao Lu Qiang Zhou Tian’en Chen Junhui Li Shuwen Jiang Qin Gao Cong Wang Dong Chen |
| author_sort | Mengyao Lu |
| collection | DOAJ |
| description | To explore the application of near-infrared (NIR) technology to the quality analysis of raw intact tobacco leaves, a nondestructive discrimination method based on NIR spectroscopy is proposed. A “multiregion + multipoint” NIR spectrum acquisition method is developed, allowing 18 NIR diffuse reflectance spectra to be collected from an intact tobacco leaf. The spectral characteristics and spectral preprocessing methods of intact tobacco leaves are analyzed, and then different spectra (independent or average spectra) and different algorithms (discriminant partial least-squares (DPLS) and Fisher’s discriminant algorithms) are used to construct discriminant models for verifying the feasibility of intact leaf modeling and determining the optimal model conditions. Qualitative discrimination models based on the position, green-variegated (GV), and the grade of intact tobacco leaves are then constructed using the NIR spectra. In the application and verification stage, a multiclassification voting mechanism is used to fuse the results of multiple spectra from a single tobacco leaf to obtain the final discrimination result for that leaf. The results show that the position-GV discrimination model constructed using independent spectra and the DPLS algorithm and the grade discrimination model constructed using independent spectra and Fisher’s algorithm achieve optimal results with intact leaf NIR wavenumbers from 5006–8988 cm−1 and the first-derivative and standard normal variate transformation preprocessing method. Finally, when applied to new tobacco leaves, the position-GV model and the grade model achieve discrimination accuracies of 95.18% and 92.77%, respectively. This demonstrates that the two models have satisfactory qualitative discrimination ability for intact tobacco leaves. This study has established a feasible method for the nondestructive qualitative discrimination of the position, GV, and grade of intact tobacco leaves based on NIR technology. |
| format | Article |
| id | doaj-art-5d0c2f6c60254b6590ea5cd2832b097f |
| institution | Kabale University |
| issn | 2314-4939 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-5d0c2f6c60254b6590ea5cd2832b097f2025-02-03T07:24:11ZengWileyJournal of Spectroscopy2314-49392021-01-01202110.1155/2021/8807199Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared TechnologyMengyao Lu0Qiang Zhou1Tian’en Chen2Junhui Li3Shuwen Jiang4Qin Gao5Cong Wang6Dong Chen7National Engineering Research Center for Information Technology in AgricultureAnhui Wannan Tobacco Co.,Ltd.National Engineering Research Center for Information Technology in AgricultureChina Agricultural UniversityNational Engineering Research Center for Information Technology in AgricultureAnhui Wannan Tobacco Co.,Ltd.National Engineering Research Center for Information Technology in AgricultureNational Engineering Research Center for Information Technology in AgricultureTo explore the application of near-infrared (NIR) technology to the quality analysis of raw intact tobacco leaves, a nondestructive discrimination method based on NIR spectroscopy is proposed. A “multiregion + multipoint” NIR spectrum acquisition method is developed, allowing 18 NIR diffuse reflectance spectra to be collected from an intact tobacco leaf. The spectral characteristics and spectral preprocessing methods of intact tobacco leaves are analyzed, and then different spectra (independent or average spectra) and different algorithms (discriminant partial least-squares (DPLS) and Fisher’s discriminant algorithms) are used to construct discriminant models for verifying the feasibility of intact leaf modeling and determining the optimal model conditions. Qualitative discrimination models based on the position, green-variegated (GV), and the grade of intact tobacco leaves are then constructed using the NIR spectra. In the application and verification stage, a multiclassification voting mechanism is used to fuse the results of multiple spectra from a single tobacco leaf to obtain the final discrimination result for that leaf. The results show that the position-GV discrimination model constructed using independent spectra and the DPLS algorithm and the grade discrimination model constructed using independent spectra and Fisher’s algorithm achieve optimal results with intact leaf NIR wavenumbers from 5006–8988 cm−1 and the first-derivative and standard normal variate transformation preprocessing method. Finally, when applied to new tobacco leaves, the position-GV model and the grade model achieve discrimination accuracies of 95.18% and 92.77%, respectively. This demonstrates that the two models have satisfactory qualitative discrimination ability for intact tobacco leaves. This study has established a feasible method for the nondestructive qualitative discrimination of the position, GV, and grade of intact tobacco leaves based on NIR technology.http://dx.doi.org/10.1155/2021/8807199 |
| spellingShingle | Mengyao Lu Qiang Zhou Tian’en Chen Junhui Li Shuwen Jiang Qin Gao Cong Wang Dong Chen Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology Journal of Spectroscopy |
| title | Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology |
| title_full | Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology |
| title_fullStr | Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology |
| title_full_unstemmed | Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology |
| title_short | Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology |
| title_sort | qualitative discrimination of intact tobacco leaves based on near infrared technology |
| url | http://dx.doi.org/10.1155/2021/8807199 |
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